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Search: Image Similarity API
Published onJan 04, 2019
Search: Image Similarity API
Goal:
This task will find the k-closest matches in the Met collection to the provided image. Users can provide either the image file or a URL to the API & the number of neighbors they would like to have returned.
API Spec: Upload image file
Request URI
POST {Endpoint}/FindSimilarImages/Url
Content Header
Name | Type | Description |
|---|---|---|
Content-type | String: ‘application/json’ | json data will be sent |
Neighbors | Int | Number of neighbors |
Example Request Body
{
"urlInput":"https://airotationstore.blob.core.windows.net/met-artworks/artwork_images/LowResolutionImages512x512_2/459033.jpg"
}
Sample Response:
{
"images": [
{
"id": "459033",
"url": "https://airotationstore.blob.core.windows.net/met-artworks/artwork_images/LowResolutionImages512x512_2/459033.jpg"
}
]
}
API Spec: Image URL
Request URI
POST {Endpoint}/FindSimilarImages/
Name | Type | Description |
|---|---|---|
Content-type | String: ‘application/json’ | json data will be sent |
Neighbors | Int | Number of neighbors |
Request Body
Name | Type | Description |
|---|---|---|
Image |
|
Sample Response:
{
"images": [
{
"id": "459033",
"url": "https://airotationstore.blob.core.windows.net/met-artworks/artwork_images/LowResolutionImages512x512_2/459033.jpg"
}
]
}
Description of the Implementation
Featurize the images in the MetCollection with ResNet50 in Keras
Train a KNN classifier on the featurized images
Create a Flask App, Dockerize and deploy as a web app on Azure
